OpenRaiser/Intern-Atlas huggingface dataset:
A Survey on Sensor-based Threats to Internet-of-Things (IoT) Devices and Applications
Venue: arXiv cs.CR 2018
Year: 2018

The concept of Internet of Things (IoT) has become more popular in the modern era of technology than ever before. From small household devices to large industrial machines, the vision of IoT has made it possible to connect the devices with the physical world around them. This increasing popularity has also made the IoT devices and applications in the center of attention among attackers. Already, several types of malicious activities exist that attempt to compromise the security and privacy of the IoT devices. One interesting emerging threat vector is the attacks that abuse the use of sensors on IoT devices. IoT devices are vulnerable to sensor-based threats due to the lack of proper security measurements available to control use of sensors by apps. By exploiting the sensors (e.g., accelerometer, gyroscope, microphone, light sensor, etc.) on an IoT device, attackers can extract information from the device, transfer malware to a device, or trigger a malicious activity to compromise the device. In this survey, we explore various threats targeting IoT devices and discuss how their sensors can be abused for malicious purposes. Specifically, we present a detailed survey about existing sensor-based threats to IoT devices and countermeasures that are developed specifically to secure the sensors of IoT devices. Furthermore, we discuss security and privacy issues of IoT devices in the context of sensor-based threats and conclude with future research directions.
Investigations on Knowledge Base Embedding for Relation Prediction and Extraction
Venue: arXiv cs.CL 2018
Year: 2018

We report an evaluation of the effectiveness of the existing knowledge base embedding models for relation prediction and for relation extraction on a wide range of benchmarks. We also describe a new benchmark, which is much larger and complex than previous ones, which we introduce to help validate the effectiveness of both tasks. The results demonstrate that knowledge base embedding models are generally effective for relation prediction but unable to give improvements for the state-of-art neural relation extraction model with the existing strategies, while pointing limitations of existing methods.
Astrophysicists and physicists as creators of ArXiv-based commenting resources for their research communities. An initial survey
Venue: arXiv cs.DL 2018
Year: 2018

This paper conveys the outcomes of what results to be the first, though initial, overview of commenting platforms and related 2.0 resources born within and for the astrophysical community (from 2004 to 2016). Experiences were added, mainly in the physics domain, for a total of 22 major items, including four epijournals, and four supplementary resources, thus casting some light onto an unexpected richness and consonance of endeavours. These experiences rest almost entirely on the contents of the database ArXiv, which adds to its merits that of potentially setting the grounds for web 2.0 resources, and research behaviours, to be explored. Most of the experiences retrieved are UK and US based, but the resulting picture is international, as various European countries, China and Australia have been actively involved. Final remarks about creation patterns and outcome of these resources are outlined. The results integrate the previous studies according to which the web 2.0 is presently of limited use for communication in astrophysics and vouch for a role of researchers in the shaping of their own professional communication tools that is greater than expected. Collaterally, some aspects of ArXiv s recent pathway towards partial inclusion of web 2.0 features are touched upon. Further investigation is hoped for.
Applications of a Graph Theoretic Based Clustering Framework in Computer Vision and Pattern Recognition
Venue: arXiv cs.CV 2018
Year: 2018

Recently, several clustering algorithms have been used to solve variety of problems from different discipline. This dissertation aims to address different challenging tasks in computer vision and pattern recognition by casting the problems as a clustering problem. We proposed novel approaches to solve multi-target tracking, visual geo-localization and outlier detection problems using a unified underlining clustering framework, i.e., dominant set clustering and its extensions, and presented a superior result over several state-of-the-art approaches.
Object Detection on Dynamic Occupancy Grid Maps Using Deep Learning and Automatic Label Generation
Venue: arXiv cs.CV 2018
Year: 2018

We tackle the problem of object detection and pose estimation in a shared space downtown environment. For perception multiple laser scanners with 360° coverage were fused in a dynamic occupancy grid map (DOGMa). A single-stage deep convolutional neural network is trained to provide object hypotheses comprising of shape, position, orientation and an existence score from a single input DOGMa. Furthermore, an algorithm for offline object extraction was developed to automatically label several hours of training data. The algorithm is based on a two-pass trajectory extraction, forward and backward in time. Typical for engineered algorithms, the automatic label generation suffers from misdetections, which makes hard negative mining impractical. Therefore, we propose a loss function counteracting the high imbalance between mostly static background and extremely rare dynamic grid cells. Experiments indicate, that the trained network has good generalization capabilities since it detects objects occasionally lost by the label algorithm. Evaluation reaches an average precision (AP) of 75.9%
Axiomatic Foundations and Algorithms for Deciding Semantic Equivalences of SQL Queries
Venue: arXiv cs.DB 2018
Year: 2018

Deciding the equivalence of SQL queries is a fundamental problem in data management. As prior work has mainly focused on studying the theoretical limitations of the problem, very few implementations for checking such equivalences exist. In this paper, we present a new formalism and implementation for reasoning about the equivalences of SQL queries. Our formalism, U-semiring, extends SQL's semiring semantics with unbounded summation and duplicate elimination. U-semiring is defined using only very few axioms and can thus be easily implemented using proof assistants such as Coq for automated query reasoning. Yet, they are sufficient enough to enable us reason about sophisticated SQL queries that are evaluated over bags and sets, along with various integrity constraints. To evaluate the effectiveness of U-semiring, we have used it to formally verify 39 query rewrite rules from both classical data management research papers and real-world SQL engines, where many of them have never been proven correct before.
Spectral Image Visualization Using Generative Adversarial Networks
Venue: arXiv cs.CV 2018
Year: 2018

Spectral images captured by satellites and radio-telescopes are analyzed to obtain information about geological compositions distributions, distant asters as well as undersea terrain. Spectral images usually contain tens to hundreds of continuous narrow spectral bands and are widely used in various fields. But the vast majority of those image signals are beyond the visible range, which calls for special visualization technique. The visualizations of spectral images shall convey as much information as possible from the original signal and facilitate image interpretation. However, most of the existing visualizatio methods display spectral images in false colors, which contradict with human's experience and expectation. In this paper, we present a novel visualization generative adversarial network (GAN) to display spectral images in natural colors. To achieve our goal, we propose a loss function which consists of an adversarial loss and a structure loss. The adversarial loss pushes our solution to the natural image distribution using a discriminator network that is trained to differentiate between false-color images and natural-color images. We also use a cycle loss as the structure constraint to guarantee structure consistency. Experimental results show that our method is able to generate structure-preserved and natural-looking visualizations.
Game Data Mining Competition on Churn Prediction and Survival Analysis using Commercial Game Log Data
Venue: arXiv cs.DB 2018
Year: 2018

Game companies avoid sharing their game data with external researchers. Only a few research groups have been granted limited access to game data so far. The reluctance of these companies to make data publicly available limits the wide use and development of data mining techniques and artificial intelligence research specific to the game industry. In this work, we developed and implemented an international competition on game data mining using commercial game log data from one of the major game companies in South Korea: NCSOFT. Our approach enabled researchers to develop and apply state-of-the-art data mining techniques to game log data by making the data open. For the competition, data were collected from Blade & Soul, an action role-playing game, from NCSOFT. The data comprised approximately 100 GB of game logs from 10,000 players. The main aim of the competition was to predict whether a player would churn and when the player would churn during two periods between which the business model was changed to a free-to-play model from a monthly subscription. The results of the competition revealed that highly ranked competitors used deep learning, tree boosting, and linear regression.
On Synthesis of Reversible Circuits with Small Number of Additional Inputs Consisting of NOT, CNOT and 2-CNOT Gates
Venue: arXiv cs.CC 2018
Year: 2018

The paper discusses the gate complexity of reversible circuits with the small number of additional inputs consisting of NOT, CNOT and 2-CNOT gates. We study Shannon's gate complexity function $L(n, q)$ for a reversible circuit implementing a Boolean transformation $f\colon \mathbb Z_2^n \to \mathbb Z_2^n$ with $q \leqslant O(n^2)$ additional inputs. The general bound $L(n,q) \asymp n2^n \mathop / \log_2 n$ is proved for this case.
Experience Report: Formal Methods in Material Science
Venue: arXiv cs.SE 2018
Year: 2018

Increased demands in the field of scientific computation require that algorithms be more efficiently implemented. Maintaining correctness in addition to efficiency is a challenge that software engineers in the field have to face. In this report we share our first impressions and experiences on the applicability of formal methods to such design challenges arising in the development of scientific computation software in the field of material science. We investigated two different algorithms, one for load distribution and one for the computation of convex hulls, and demonstrate how formal methods have been used to discover counterexamples to the correctness of the existing implementations as well as proving the correctness of a revised algorithm. The techniques employed for this include SMT solvers, and automatic and interactive verification tools.
When is the condition of order preservation met?
Venue: arXiv cs.DM 2018
Year: 2018

This article explores a relationship between inconsistency in the pairwise comparisons method and conditions of order preservation. A pairwise comparisons matrix with elements from an alo-group is investigated. This approach allows for a generalization of previous results. Sufficient conditions for order preservation based on the properties of elements of pairwise comparisons matrix are derived. A numerical example is presented.
Efficient Learning of Bounded-Treewidth Bayesian Networks from Complete and Incomplete Data Sets
Venue: arXiv cs.AI 2018
Year: 2018

Learning a Bayesian networks with bounded treewidth is important for reducing the complexity of the inferences. We present a novel anytime algorithm (k-MAX) method for this task, which scales up to thousands of variables. Through extensive experiments we show that it consistently yields higher-scoring structures than its competitors on complete data sets. We then consider the problem of structure learning from incomplete data sets. This can be addressed by structural EM, which however is computationally very demanding. We thus adopt the novel k-MAX algorithm in the maximization step of structural EM, obtaining an efficient computation of the expected sufficient statistics. We test the resulting structural EM method on the task of imputing missing data, comparing it against the state-of-the-art approach based on random forests. Our approach achieves the same imputation accuracy of the competitors, but in about one tenth of the time. Furthermore we show that it has worst-case complexity linear in the input size, and that it is easily parallelizable.
FixaTons: A collection of Human Fixations Datasets and Metrics for Scanpath Similarity
Venue: arXiv cs.AI 2018
Year: 2018

In the last three decades, human visual attention has been a topic of great interest in various disciplines. In computer vision, many models have been proposed to predict the distribution of human fixations on a visual stimulus. Recently, thanks to the creation of large collections of data, machine learning algorithms have obtained state-of-the-art performance on the task of saliency map estimation. On the other hand, computational models of scanpath are much less studied. Works are often only descriptive or task specific. This is due to the fact that the scanpath is harder to model because it must include the description of a dynamic. General purpose computational models are present in the literature, but are then evaluated in tasks of saliency prediction, losing therefore information about the dynamics and the behaviour. In addition, two technical reasons have limited the research. The first reason is the lack of robust and uniformly used set of metrics to compare the similarity between scanpath. The second reason is the lack of sufficiently large and varied scanpath datasets. In this report we want to help in both directions. We present FixaTons, a large collection of datasets human scanpaths (temporally ordered sequences of fixations) and saliency maps. It comes along with a software library for easy data usage, statistics calculation and implementation of metrics for scanpath and saliency prediction evaluation.
Deep Versus Wide Convolutional Neural Networks for Object Recognition on Neuromorphic System
Venue: arXiv cs.CV 2018
Year: 2018

In the last decade, special purpose computing systems, such as Neuromorphic computing, have become very popular in the field of computer vision and machine learning for classification tasks. In 2015, IBM's released the TrueNorth Neuromorphic system, kick-starting a new era of Neuromorphic computing. Alternatively, Deep Learning approaches such as Deep Convolutional Neural Networks (DCNN) show almost human-level accuracies for detection and classification tasks. IBM's 2016 release of a deep learning framework for DCNNs, called Energy Efficient Deep Neuromorphic Networks (Eedn). Eedn shows promise for delivering high accuracies across a number of different benchmarks, while consuming very low power, using IBM's TrueNorth chip. However, there are many things that remained undiscovered using the Eedn framework for classification tasks on a Neuromorphic system. In this paper, we have empirically evaluated the performance of different DCNN architectures implemented within the Eedn framework. The goal of this work was discover the most efficient way to implement DCNN models for object classification tasks using the TrueNorth system. We performed our experiments using benchmark data sets such as MNIST, COIL 20, and COIL 100. The experimental results show very promising classification accuracies with very low power consumption on IBM's NS1e Neurosynaptic system. The results show that for datasets with large numbers of classes, wider networks perform better when compared to deep networks comprised of nearly the same core complexity on IBM's TrueNorth system.
High Performance Rearrangement and Multiplication Routines for Sparse Tensor Arithmetic
Venue: arXiv cs.MS 2018
Year: 2018

Researchers are increasingly incorporating numeric high-order data, i.e., numeric tensors, within their practice. Just like the matrix/vector (MV) paradigm, the development of multi-purpose, but high-performance, sparse data structures and algorithms for arithmetic calculations, e.g., those found in Einstein-like notation, is crucial for the continued adoption of tensors. We use the example of high-order differential operators to illustrate this need. As sparse tensor arithmetic is an emerging research topic, with challenges distinct from the MV paradigm, many aspects require further articulation. We focus on three core facets. First, aligning with prominent voices in the field, we emphasise the importance of data structures able to accommodate the operational complexity of tensor arithmetic. However, we describe a linearised coordinate (LCO) data structure that provides faster and more memory-efficient sorting performance. Second, flexible data structures, like the LCO, rely heavily on sorts and permutations. We introduce an innovative permutation algorithm, based on radix sort, that is tailored to rearrange already-sorted sparse data, producing significant performance gains. Third, we introduce a novel poly-algorithm for sparse tensor products, where hyper-sparsity is a possibility. Different manifestations of hyper-sparsity demand their own approach, which our poly-algorithm is the first to provide. These developments are incorporated within our LibNT and NTToolbox software libraries. Benchmarks, frequently drawn from the high-order differential operators example, demonstrate the practical impact of our routines, with speed-ups of 40% or higher compared to alternative high-performance implementations. Comparisons against the MATLAB Tensor Toolbox show over 10 times speed improvements. Thus, these advancements produce significant practical improvements for sparse tensor arithmetic.
Nonlinear Model Predictive Guidance for Fixed-wing UAVs Using Identified Control Augmented Dynamics
Venue: arXiv cs.RO 2018
Year: 2018

As off-the-shelf (OTS) autopilots become more widely available and user-friendly and the drone market expands, safer, more efficient, and more complex motion planning and control will become necessary for fixed-wing aerial robotic platforms. Considering typical low-level attitude stabilization available on OTS flight controllers, this paper first develops an approach for modeling and identification of the control augmented dynamics for a small fixed-wing Unmanned Aerial Vehicle (UAV). A high-level Nonlinear Model Predictive Controller (NMPC) is subsequently formulated for simultaneous airspeed stabilization, path following, and soft constraint handling, using the identified model for horizon propagation. The approach is explored in several exemplary flight experiments including path following of helix and connected Dubins Aircraft segments in high winds as well as a motor failure scenario. The cost function, insights on its weighting, and additional soft constraints used throughout the experimentation are discussed.
Social Media Data Analysis and Feedback for Advanced Disaster Risk Management
Venue: arXiv cs.SI 2018
Year: 2018

Social media are more than just a one-way communication channel. Data can be collected, analyzed and contextualized to support disaster risk management. However, disaster management agencies typically use such added-value information to support only their own decisions. A feedback loop between contextualized information and data suppliers would result in various advantages. First, it could facilitate the near real-time communication of early warnings derived from social media, linked to other sources of information. Second, it could support the staff of aid organizations during response operations. Based on the example of Hurricanes Harvey and Irma we show how filtered, geolocated Tweets can be used for rapid damage assessment. We claim that the next generation of big data analyses will have to generate actionable information resulting from the application of advanced analytical techniques. These applications could include the provision of social media-based training data for algorithms designed to forecast actual cyclone impacts or new socio-economic validation metrics for seasonal climate forecasts.
A Patterns Based Approach for Design of Educational Technologies
Venue: arXiv cs.SE 2018
Year: 2018

Instructional design is a fundamental base for educational technologies as it lays the foundation to facilitate learning and teaching based on pedagogical underpinnings. However, most of the educational technologies today face two core challenges in this context: (i) lack of instructional design as a basis (ii) lack of support for a variety of instructional designs. In order to address these challenges, we propose a patterns based approach for design of educational technologies. This is in contrast with existing literature that focuses either on patterns in education or in software, and not both. The core idea of our approach is to leverage patterns for modeling instructional design knowledge and to connect it with patterns in software architecture. We discuss different categories of patterns in instructional design. We then present the notion of Pattern-Oriented Instructional Design (POID) as a way to model instructional design as a connection of patterns (GoalPattern, ProcessPattern, ContentPattern) and integrate it with Pattern-Oriented Software Architecture (POSA) based on fundamental principles in software engineering. We demonstrate our approach through adult literacy case study (287 million learners, 22 Indian Languages and a variety of instructional designs). The results of our approach (both web and mobile versions) are available at http://rice.iiit.ac.in and were adopted by National Literacy Mission Authority of Government of India.
Stubborn Transaction Reduction (with Proofs)
Venue: arXiv cs.LO 2018
Year: 2018

The exponential explosion of parallel interleavings remains a fundamental challenge to model checking of concurrent programs. Both partial-order reduction (POR) and transaction reduction (TR) decrease the number of interleavings in a concurrent system. Unlike POR, transactions also reduce the number of intermediate states. Modern POR techniques, on the other hand, offer more dynamic ways of identifying commutative behavior, a crucial task for obtaining good reductions. We show that transaction reduction can use the same dynamic commutativity as found in stubborn set POR. We also compare reductions obtained by POR and TR, demonstrating with several examples that these techniques complement each other. With an implementation of the dynamic transactions in the model checker LTSmin, we compare its effectiveness with the original static TR and two POR approaches. Several inputs, including realistic case studies, demonstrate that the new dynamic TR can surpass POR in practice.
Monopoly pricing with buyer search
Venue: arXiv cs.GT 2018
Year: 2018

In many shopping scenarios, e.g., in online shopping, customers have a large menu of options to choose from. However, most of the buyers do not browse all the options and make decision after considering only a small part of the menu. To study such buyer's behavior we consider the standard Bayesian monopoly problem for a unit-demand buyer, where the monopolist displays the menu dynamically page after a page to the buyer. The seller aims to maximize the expected revenue over the distribution of buyer's values which we assume are i.i.d. The buyer incurs a fixed cost for browsing through one menu page and would stop if that cost exceeds the increase in her utility. We observe that the optimal posted price mechanism in our dynamic setting may have quite different structure than in the classic static scenario. We find a (relatively) simple and approximately optimal mechanism, that uses part of the items as a "bait" to keep the buyer interested for multiple rounds with low prices, while at the same time showing many other expensive items.